Atherosclerotic cardiovascular disease (ASCVD) is the most important cause of morbidity and mortality nationally and internationally. Improving ASCVD risk prediction is a high clinical priority. We sought to determine which of 3 ASCVD risk scores best predicts the need for revascularization and incident major adverse coronary events (MACE) in symptomatic patients at low-to-intermediate primary ASCVD risk referred for regadenoson-stress positron emission tomography (PET). Risk scores included the standard ASCVD pooled cohort equation (PCE), the multiethnic study of atherosclerosis (MESA) risk equation, and the coronary artery calcium score (CACS), obtained by PET. All qualifying patients in our institution at primary ASCVD risk referred for PET-stress tests in whom PCE, MESA, and CAC scores could be calculated were studied. CACS categories were: 0, 1 to 10, 11 to 299, 300 to 999, and 1000+. MESA and PCE scores were divided into quartiles. Logistic regression modeling was used to predict clinical/PET-driven early revascularization (within 90 days) and 1-year MACE (death, myocardial infarction, or any-time revascularization). A total of 981 patients (54% men, age 67 ± 10 years) qualified and were studied. Scores including CAC (MESA, CACS) performed better than PCE for predicting overall 1-year MACE (MESA p <0.001, CACS p = 0.012 vs PCE), which was driven by early revascularization. In conclusion, in a large population of patients at primary ASCVD risk referred for PET-stress testing, risk scores including CAC (CACS, MESA), which better predicted early revascularization and 1-year MACE, may be particularly useful in primary coronary risk assessment when considering whom to refer for PET-stress testing.
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http://dx.doi.org/10.1016/j.amjcard.2019.10.044 | DOI Listing |
Ann Endocrinol (Paris)
January 2025
Assistance Publique Hôpitaux de Paris, Pituitary Unit, Pitié-Salpêtrière Hospital, 75013 Paris, France. Electronic address:
Background: Non-functional adrenal incidentaloma (NFAI) is associated with increased risk of adverse cardiometabolic outcome. Identifying predictors of atherosclerotic cardiovascular disease (ASCVD) may enable more appropriate management strategies in patients with NFAI. We aimed to investigate body composition parameters and ASCVD risk in patients with NFAI.
View Article and Find Full Text PDFAtherosclerosis
December 2024
Rehabilitation Division, Sheba Medical Center, Tel-Hashomer, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Background And Aims: Several systemic autoimmune diseases predispose to the enhancement of Atherosclerotic Cardiovascular Disease (ASCVD). These findings underline the role of inflammation in atherogenesis. Dermatomyositis (DM) and polymyositis (PM) are polygenic autoimmune disorders involving mainly skeletal muscles.
View Article and Find Full Text PDFJACC Adv
December 2024
Johns Hopkins Department of Internal Medicine, Baltimore, Maryland, USA.
Background: Despite implementation of preventive interventions targeting cardiovascular disease (CVD), atherosclerotic CVD (ASCVD) remains a major public health concern in the South Asian (SA) population.
Objectives: The purpose of this study was to assess the risk factor prevalence and ASCVD outcomes in SA population in the United States.
Methods: The DIL Wellness and Arterial health Longitudinal Evaluation registry collected data retrospectively on SA adult patients receiving care in the Baylor Scott & White Healthcare system.
JACC Adv
December 2024
Division of Cardiology, Department of Medicine, Tufts University School of Medicine, Boston, Massachusetts, USA.
JACC Adv
December 2024
Division of Cardiovascular Disease, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Background: The Predicting Risk of CVD Events (PREVENT) equations were developed to address limitations of the Pooled Cohort Equations (PCEs) in predicting atherosclerotic cardiovascular disease (ASCVD) risk. The comparative effectiveness of the PREVENT equations versus the PCEs in predicting mortality risk remains unknown.
Objectives: The purpose of this study was to compare the risk discrimination value of the PREVENT equations with the PCEs for predicting mortality.
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